Varroa mite mode of inheritance
Nurit Eliash
1/24/2022
In this study we explored the mode of genetic inheritance of Varroa destructor, a parasitic mite of honeybees, so far known as a haplodiploid species with a arrhenotokous parthenogenesis reproductive mode. The mite shows contrasting phenomena of highly inbreeding life style (sib-mating), yet maintaining relatively high genetic variation.
The experimental setup consisted by a three-generational pedigree
with sib-mating.
Sample size: 30 families, total of 223 individuals 3 different colonies,
from OIST apiary
For details of mite collection and pedigree construction, please see the
original manuscript.
load libraries
library("tidyverse")
library("plyr")
library("dplyr")
library("ggplot2")
library("scales")
library("ggpubr")
library("gridExtra")
library("grid")
library("GGally")
library("vcfR") # for extracting genotype data from a vcf file
library("data.table")
library("stringr")
library("janitor")
library("gmodels")
library("rstatix")
library("freqtables")
library("broom")
library("DescTools") # for the Goodness-of-Fit test, 3 variables; and for Breslow-Day Test for Homogeneity of the Odds Ratios
library("vcd") # for the woolf test testign log odds for each pair
library("patchwork") # for gathering the plots
#library("fuzzyjoin") # to join tables based on a string in a column
#library("aspi") # Repeated G–tests of Goodness-of-Fit, work only for 2 variables..
#library("RVAideMemoire") # Repeated G–tests of Goodness-of-Fit, work only for 2 variables...
#library("InfoTrad")
#library("ggthemes") # for more colors in the ggplot
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE,
fig.width = 8,
fig.asp = 0.6,
out.width = "100%")
#fig.width = 6,fig.asp = 0.8,out.width = "100%"Load Variant Call Format (VCF) file.
Extract genotypes for each site and individual. The metadata for all samples can be found in here.
vcf <- read.vcfR("/Users/nuriteliash/Documents/GitHub/varroa-linkage-map/data/vcf_filter/Q40BIALLDP16HDP40mis.5Chr7/Q40BIALLDP16HDP40mis.5Chr7.recode.vcf", verbose = FALSE )
vcf## ***** Object of Class vcfR *****
## 223 samples
## 7 CHROMs
## 35,169 variants
## Object size: 293.5 Mb
## 0 percent missing data
## ***** ***** *****# extract the genotype for each site in each individual
gt <- extract.gt(vcf, element = "GT")
gt <- as.data.frame(t(gt)) %>%
rownames_to_column("ID")
#clean the ID names
gt$ID <- sub("_[^_]+$", "", gt$ID)The individual ID name nomenclature is composed of 3 parts, separated
by an underscore (_).
The first part is the family ID, the second is the unique name of the
individual, and the the third part indicates its generation, sex, and
its position in relation to the foundress mite (generation F0).
For example:
Individual ID 240_240a_son, belong to family 240, has
a unique name of 240a and is the son of the foundress
mite of family 240, that is, its a male of F1 generation.
Individual ID 240_241b_grndat, belong to family 240,
has a unique name of 241b and is the granddaughter of
the foundress mite of family 240, that is, its a female of F2 generation
(and the daughter of 240_240a_son).
From the 223 individuals of 30 families, we excluded non adults mites
(for which its hard to determine the sex).
Eventually, we kept 144 individuals (adult males and females in F0, F1
and F2 generations) of 26 families, and all 35,169 biallelic sites.
For example, these are the first 6 sites (13565-25513) of chromosome (NW_019211454.1), in one family (family number 240).
table <- gt %>%
t() %>%
as.data.frame() %>%
row_to_names(row_number = 1) %>%
dplyr::select(contains(c("son", "dat", "fnd"))) # keep only adults of F0, F1 and F2
table %>% select(contains(c("240_"))) %>% head()## 240_240a_son 240_241c_grnson 240_241b_grndat
## NW_019211454.1_13565 0/1 <NA> <NA>
## NW_019211454.1_18037 0/0 <NA> <NA>
## NW_019211454.1_20998 <NA> <NA> <NA>
## NW_019211454.1_24935 <NA> 0/0 <NA>
## NW_019211454.1_25470 <NA> <NA> <NA>
## NW_019211454.1_25513 <NA> <NA> <NA>
## 240_241a_grndat 240_241_dat 240_240_fnd
## NW_019211454.1_13565 0/1 0/1 0/0
## NW_019211454.1_18037 0/0 0/0 0/0
## NW_019211454.1_20998 0/0 0/0 0/0
## NW_019211454.1_24935 0/0 0/0 0/0
## NW_019211454.1_25470 0/0 0/0 0/0
## NW_019211454.1_25513 0/0 <NA> 0/0The family members include:
- F0 generation: foundress female mite
(240_240_fnd).
- F1 generation: adult son (240_240a_son) and adult
daughter (240_241_dat) of the foundress F0 mite.
Because in varroa mite reproduction is via sib-mating, these brother and
sister will also mate, to produce the F2 generation.
- F2 generation: adult grandson (240_241c_grnson) and two adult granddaughters (240_241a_grndat and 240_241b_grndat), of the foundress F0 mite.
Each individual, was genotyped for each site with one of the three genotypes:
- Homozygote for the reference allele = 0/0
- Heterozygote = 0/1
- Homozygote for the alternate allele = 1/1
- “NA” = site genotype not determined
For more information about the mapping, variant calling and variant filtration, please see the Snakemake pipeline in here.
In the following code we viewed the F2 offspring genotype of all possible nine crosses between F1 male and females.
- Control crosses of homozygotic sites: The first
four crosses are aiming mainly to detect false sites:
- F1 male (0/0) x female (0/0)
- F1 male (1/1) x female (1/1)
- F1 male (0/0) x female (1/1)
- F1 male (1/1) x female (0/0)
Then we cross heterozygotic sites, to explore the mode of genetic inheritance in varroa mite:
- Homozygotic male, with heterozygotic female:
- F1 male (0/0) x female (0/1)
- F1 male (1/1) x female (0/1)
- Heterozygotic male, with homozygotic female:
- F1 male (0/1) x female (0/0)
- F1 male (0/1) x female (1/1)
- Heterozygotic male, with heterozygotic female:
- F1 male (0/1) x female (0/1)
For each crossing we have expected genotype-ratio of the F2, based on the following assumptions under arrhenotokous haplodiploid system:
- Fertilized egg develops into diploid female ♀ (2n).
- Unfertilized egg develops into haploid male ♂ (n).
- No paternal inheritance in the males.
Control: Homozygotic sites (crosses 1-4)
To detect false sites
(1) F1 male (0/0) x female (0/0)
F2 offspring
we expect all F2 offspring to be homozygotic (0/0) like their parents (F1)
x <- dat %>%
dplyr::select(c("sample","gt","sex", "exp","prop", "total")) %>%
mutate(type = "exp") %>%
dplyr::rename(count = exp)
y <- dat %>%
dplyr::select(c("sample","gt","sex", "obs","total")) %>%
mutate(type = "obs") %>%
dplyr::rename(count = obs) %>%
mutate(prop = (count/total))
df_male <- rbind(x, y) %>%
dplyr::filter(sex == "male")
df_fem <- rbind(x, y) %>%
dplyr::filter(sex == "female")
p_male_pooled <- ggplot(df_male, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("F2 males") +
ylab("Proportion of F2 genotype") +
labs(title = "F2 Males of F1 male (0/0) x female (0/0)",
subtitle = paste0("Pooled sites = ", df_male %>% dplyr::filter(gt == "0/1") %>% dplyr::filter(type == "obs") %>% summarise(sum(total)) %>% as.numeric())) +
scale_x_discrete("type", labels = c("Expected","Observed")) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_text(size=15),
axis.title.x = element_blank())
p_female_pooled <- ggplot(df_fem, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("F2 females") +
ylab("Proportion of F2 genotype") +
labs(title = "F2 females of F1 male (0/0) x female (0/0)",
subtitle = paste0("Pooled sites = ", df_fem %>% dplyr::filter(gt == "0/1") %>% dplyr::filter(type == "obs") %>% summarise(sum(total)) %>% as.numeric())) +
scale_x_discrete("type", labels = c("Expected","Observed")) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_text(size=15),
axis.title.x = element_blank())
grid.arrange(p_female_pooled, p_male_pooled, ncol = 2)
# plot ratio for each individual:
# males:
ggplot(df_male, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("F2 siblings") +
ylab("Proportion of F2 genotype") +
labs(title = "F2 Males of F1 male (0/0) x female (0/0)") +
# geom_text(aes(label = n), position = position_stack(vjust = 0.5)) +
geom_text(data = filter(df_male, gt == "1/1"), aes(y=prop, x=type, label=total), vjust = 0) +
facet_wrap(~ sample) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_text(size=15),
axis.title.x = element_blank()) 
# and females:
ggplot(df_fem, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("F2 siblings") +
ylab("Proportion of F2 genotype") +
labs(title = "F2 Females of F1 male (0/0) x female (0/0)") +
# geom_text(aes(label = n), position = position_stack(vjust = 0.5)) +
geom_text(data = filter(df_fem, gt == "1/1"), aes(y=prop, x=type, label=total), vjust = 0) +
facet_wrap(~ sample) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_text(size=15),
axis.title.x = element_blank()) 
F0 foundress
we expect the F0 foundress to be homozygotic (0/0), like her son
# plot the pooled data for all F0 foundress with male (0/0) x female (0/0) offspring
ggplot(observed, aes(fill=gt, y=prop, x = type)) +
geom_bar(position="fill", stat="identity") +
ylab("Proportion of F0 genotype") +
labs(title = "F0 foundress of F1 male (0/0) x female (0/0)",
subtitle = paste0("Pooled sites = ", observed %>% dplyr::filter(gt == "0/1") %>% summarise(sum(total)) %>% as.numeric())) +
theme_classic() +
theme(axis.text.x = element_blank(), axis.title.x = element_blank()) +
labs(fill = "Genotype")
# plot for each individual:
ggplot(observed, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("Observed") +
ylab("Proportion of F0 genotype") +
labs(title = "F0 foundress of F1 male (0/0) x female (0/0)") +
geom_text(data = filter(observed, gt == "1/1"), aes(y=prop, x=type, label=total), vjust = 0) +
facet_wrap(~ sample) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_blank(), axis.title.x = element_blank()) 
(2) F1 male (1/1) x female (1/1)
F2 offspring
we expect all F2 offspring to be homozygotic (1/1) like their parents (F1)
# set the families (include only families with at least one adult F2)
family = grep("grndat|grnson",gt$ID, value=TRUE) %>%
str_extract("[^_]+") %>%
unique()
# define a list to put all the data frames in
obs <- list()
for (fam in family) {
obs[[fam]] <- table %>%
dplyr::select(starts_with(fam)) %>%
dplyr::filter_at(vars(matches("_son")), all_vars(. == "1/1")) %>%
dplyr::filter_at(vars(matches("_dat")), all_vars(. == "1/1")) %>%
dplyr::select(contains("grn")) %>%
tidyr::pivot_longer(everything()) %>%
dplyr::rename(sample = name, gt = value) %>%
dplyr::count(sample, gt, .drop = FALSE) %>%
dplyr::filter(gt %in% c("0/0", "1/1", "0/1")) %>%
mutate(n = as.numeric(n)) %>%
group_by(sample) %>%
mutate(total = as.numeric(sum(n))) %>%
dplyr::rename(obs = n) %>%
mutate(sex = case_when(
grepl("son", sample) ~ "male",
grepl("dat", sample) ~ "female"))
}
# bind all families together, to a final data frame containing all observed counts
observed <- do.call("rbind", obs)
# state the expected site proportion of each genotype and sex under a perfect mendelian segregation of F2
fem_exp <- data.frame(sex = "female", gt = c("0/0", "0/1", "1/1") , prop = c(0,0,1))
male_exp <- data.frame(sex = "male",gt = c("0/0", "0/1", "1/1"),prop = c(0,0,1))
mendel <- bind_rows(fem_exp,male_exp) %>% mutate(sex = as.character(sex))%>% mutate(gt = as.character(gt))
# mutate(type = "exp")
# using on the total genotypes count of sites per sample, calculate the expected counts, and add them to the observed counts into a final dat table
dat <- observed %>%
#mutate(sample = as.character(sample)) %>%
mutate(gt = as.character(gt)) %>%
left_join(mendel, by = c("gt", "sex")) %>%
mutate(exp = total*prop)x <- dat %>%
dplyr::select(c("sample","gt","sex", "exp","prop", "total")) %>%
mutate(type = "exp") %>%
dplyr::rename(count = exp)
y <- dat %>%
dplyr::select(c("sample","gt","sex", "obs","total")) %>%
mutate(type = "obs") %>%
dplyr::rename(count = obs) %>%
mutate(prop = (count/total))
df_male <- rbind(x, y) %>%
dplyr::filter(sex == "male")
df_fem <- rbind(x, y) %>%
dplyr::filter(sex == "female")
p_male_pooled <- ggplot(df_male, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("F2 males") +
ylab("Proportion of F2 genotype") +
labs(title = "F2 Males of F1 male (1/1) x female (1/1)",
subtitle = paste0("Pooled sites = ", df_male %>% dplyr::filter(gt == "0/0") %>% dplyr::filter(type == "obs") %>% summarise(sum(total)) %>% as.numeric())) +
scale_x_discrete("type", labels = c("Expected","Observed")) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_text(size=15),
axis.title.x = element_blank())
p_female_pooled <- ggplot(df_fem, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("F2 females") +
ylab("Proportion of F2 genotype") +
labs(title = "F2 females of F1 male (1/1) x female (1/1)",
subtitle = paste0("Pooled sites = ", df_fem %>% dplyr::filter(gt == "0/0") %>% dplyr::filter(type == "obs") %>% summarise(sum(total)) %>% as.numeric())) +
scale_x_discrete("type", labels = c("Expected","Observed")) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_text(size=15),
axis.title.x = element_blank())
grid.arrange(p_female_pooled, p_male_pooled, ncol = 2)
ggplot(df_male, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("F2 siblings") +
ylab("Proportion of F2 genotype") +
labs(title = "F2 Males of F1 male (1/1) x female (1/1)") +
# geom_text(aes(label = n), position = position_stack(vjust = 0.5)) +
geom_text(data = filter(df_male, gt == "1/1"), aes(y=prop, x=type, label=total), vjust = 0) +
facet_wrap(~ sample) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_text(size=15),
axis.title.x = element_blank()) 
ggplot(df_fem, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("F2 siblings") +
ylab("Proportion of F2 genotype") +
labs(title = "F2 Females of F1 male (1/1) x female (1/1)") +
# geom_text(aes(label = n), position = position_stack(vjust = 0.5)) +
geom_text(data = filter(df_fem, gt == "1/1"), aes(y=prop, x=type, label=total), vjust = 0) +
facet_wrap(~ sample) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_text(size=15),
axis.title.x = element_blank()) 
F0 foundress
we expect the F0 foundress to be homozygotic (1/1), like her son
# plot the pooled data for all F0 foundress with male (1/1) x female (1/1) offspring
ggplot(observed, aes(fill=gt, y=prop, x = type)) +
geom_bar(position="fill", stat="identity") +
ylab("Proportion of F0 genotype") +
labs(title = "F0 foundress of F1 male (1/1) x female (1/1)",
subtitle = paste0("Pooled sites = ", observed %>% dplyr::filter(gt == "0/1") %>% summarise(sum(total)) %>% as.numeric())) +
theme_classic() +
theme(axis.text.x = element_blank(), axis.title.x = element_blank()) +
labs(fill = "Genotype")
# plot for each individual:
ggplot(observed, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("Observed") +
ylab("Proportion of F0 genotype") +
labs(title = "F0 foundress of F1 male (1/1) x female (1/1)") +
geom_text(data = filter(observed, gt == "1/1"), aes(y=prop, x=type, label=total), vjust = 0) +
facet_wrap(~ sample) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_blank(), axis.title.x = element_blank()) 
(3) F1 male (0/0) x female (1/1)
F2 offpsring
We expect zero sites for this cross, since there is no paternal
inheritance to the males.
Indeed, there are only 103 sites in the F2 pooled females, and 71 for
the F2 pooled males:
# set the families (include only families with at least one adult F2)
family = grep("grndat|grnson",gt$ID, value=TRUE) %>%
str_extract("[^_]+") %>%
unique()
# define a list to put all the data frames in
obs <- list()
for (fam in family) {
obs[[fam]] <- table %>%
dplyr::select(starts_with(fam)) %>%
dplyr::filter_at(vars(matches("_son")), all_vars(. == "0/0")) %>%
dplyr::filter_at(vars(matches("_dat")), all_vars(. == "1/1")) %>%
dplyr::select(contains("grn")) %>%
tidyr::pivot_longer(everything()) %>%
dplyr::rename(sample = name, gt = value) %>%
dplyr::count(sample, gt, .drop = FALSE) %>%
dplyr::filter(gt %in% c("0/0", "1/1", "0/1")) %>%
mutate(n = as.numeric(n)) %>%
group_by(sample) %>%
mutate(total = as.numeric(sum(n))) %>%
dplyr::rename(obs = n) %>%
mutate(sex = case_when(
grepl("son", sample) ~ "male",
grepl("dat", sample) ~ "female"))
}
# bind all families together, to a final data frame containing all observed counts
observed <- do.call("rbind", obs)
# state the expected site proportion of each genotype and sex under a perfect mendelian segregation of F2
fem_exp <- data.frame(sex = "female", gt = c("0/0", "0/1", "1/1") , prop = c(0,0,0))
male_exp <- data.frame(sex = "male",gt = c("0/0", "0/1", "1/1"),prop = c(0,0,0))
mendel <- bind_rows(fem_exp,male_exp) %>% mutate(sex = as.character(sex))%>% mutate(gt = as.character(gt))
# mutate(type = "exp")
# using on the total genotypes count of sites per sample, calculate the expected counts, and add them to the observed counts into a final dat table
dat <- observed %>%
#mutate(sample = as.character(sample)) %>%
mutate(gt = as.character(gt)) %>%
left_join(mendel, by = c("gt", "sex")) %>%
mutate(exp = total*prop)x <- dat %>%
dplyr::select(c("sample","gt","sex", "exp","prop", "total")) %>%
mutate(type = "exp") %>%
dplyr::rename(count = exp)
y <- dat %>%
dplyr::select(c("sample","gt","sex", "obs","total")) %>%
mutate(type = "obs") %>%
dplyr::rename(count = obs) %>%
mutate(prop = (count/total))
df_male <- rbind(x, y) %>%
dplyr::filter(sex == "male")
df_fem <- rbind(x, y) %>%
dplyr::filter(sex == "female")
p_male_pooled <- ggplot(df_male, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("F2 males") +
ylab("Proportion of F2 genotype") +
labs(title = "F2 Males of F1 male (0/0) x female (1/1)",
subtitle = paste0("Pooled sites = ", df_male %>% dplyr::filter(gt == "0/0") %>% dplyr::filter(type == "obs") %>% summarise(sum(total)) %>% as.numeric())) +
scale_x_discrete("type", labels = c("Expected","Observed")) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_text(size=15),
axis.title.x = element_blank())
p_female_pooled <- ggplot(df_fem, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("F2 females") +
ylab("Proportion of F2 genotype") +
labs(title = "F2 females of F1 male (0/0) x female (1/1)",
subtitle = paste0("Pooled sites = ", df_fem %>% dplyr::filter(gt == "0/0") %>% dplyr::filter(type == "obs") %>% summarise(sum(total)) %>% as.numeric())) +
scale_x_discrete("type", labels = c("Expected","Observed")) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_text(size=15),
axis.title.x = element_blank())
grid.arrange(p_female_pooled, p_male_pooled, ncol = 2)
ggplot(df_male, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("F2 siblings") +
ylab("Proportion of F2 genotype") +
labs(title = "F2 Males of F1 male (0/0) x female (1/1)") +
# geom_text(aes(label = n), position = position_stack(vjust = 0.5)) +
geom_text(data = filter(df_male, gt == "1/1"), aes(y=prop, x=type, label=total), vjust = 0) +
facet_wrap(~ sample) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_text(size=15),
axis.title.x = element_blank()) 
ggplot(df_fem, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("F2 siblings") +
ylab("Proportion of F2 genotype") +
labs(title = "F2 Females of F1 male (0/0) x female (1/1)") +
# geom_text(aes(label = n), position = position_stack(vjust = 0.5)) +
geom_text(data = filter(df_fem, gt == "1/1"), aes(y=prop, x=type, label=total), vjust = 0) +
facet_wrap(~ sample) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_text(size=15),
axis.title.x = element_blank()) 
F0 foundress
we expect ?????…
# plot the pooled data for all F0 foundress with male (0/0) x female (1/1) offspring
ggplot(observed, aes(fill=gt, y=prop, x = type)) +
geom_bar(position="fill", stat="identity") +
ylab("Proportion of F0 genotype") +
labs(title = "F0 foundress of F1 male (0/0) x female (1/1)",
subtitle = paste0("Pooled sites = ", observed %>% dplyr::filter(gt == "0/1") %>% summarise(sum(total)) %>% as.numeric())) +
theme_classic() +
theme(axis.text.x = element_blank(), axis.title.x = element_blank()) +
labs(fill = "Genotype")
# plot for each individual:
ggplot(observed, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("Observed") +
ylab("Proportion of F0 genotype") +
labs(title = "F0 foundress of F1 male (0/0) x female (1/1)") +
geom_text(data = filter(observed, gt == "1/1"), aes(y=prop, x=type, label=total), vjust = 0) +
facet_wrap(~ sample) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_blank(), axis.title.x = element_blank()) 
(4) F1 male (1/1) x female (0/0)
F2 offspring
We expect zero sites for this cross, since there is no paternal
inheritance to the males.
Indeed, there are only 98 sites in the F2 pooled females, and 75 for the
F2 pooled males:
# set the families (include only families with at least one adult F2)
family = grep("grndat|grnson",gt$ID, value=TRUE) %>%
str_extract("[^_]+") %>%
unique()
# define a list to put all the data frames in
obs <- list()
for (fam in family) {
obs[[fam]] <- table %>%
dplyr::select(starts_with(fam)) %>%
dplyr::filter_at(vars(matches("_son")), all_vars(. == "1/1")) %>%
dplyr::filter_at(vars(matches("_dat")), all_vars(. == "0/0")) %>%
dplyr::select(contains("grn")) %>%
tidyr::pivot_longer(everything()) %>%
dplyr::rename(sample = name, gt = value) %>%
dplyr::count(sample, gt, .drop = FALSE) %>%
dplyr::filter(gt %in% c("0/0", "1/1", "0/1")) %>%
mutate(n = as.numeric(n)) %>%
group_by(sample) %>%
mutate(total = as.numeric(sum(n))) %>%
dplyr::rename(obs = n) %>%
mutate(sex = case_when(
grepl("son", sample) ~ "male",
grepl("dat", sample) ~ "female"))
}
# bind all families together, to a final data frame containing all observed counts
observed <- do.call("rbind", obs)
# state the expected site proportion of each genotype and sex under a perfect mendelian segregation of F2
fem_exp <- data.frame(sex = "female", gt = c("0/0", "0/1", "1/1") , prop = c(0,0,0))
male_exp <- data.frame(sex = "male",gt = c("0/0", "0/1", "1/1"),prop = c(0,0,0))
mendel <- bind_rows(fem_exp,male_exp) %>% mutate(sex = as.character(sex))%>% mutate(gt = as.character(gt))
# mutate(type = "exp")
# using on the total genotypes count of sites per sample, calculate the expected counts, and add them to the observed counts into a final dat table
dat <- observed %>%
#mutate(sample = as.character(sample)) %>%
mutate(gt = as.character(gt)) %>%
left_join(mendel, by = c("gt", "sex")) %>%
mutate(exp = total*prop)x <- dat %>%
dplyr::select(c("sample","gt","sex", "exp","prop", "total")) %>%
mutate(type = "exp") %>%
dplyr::rename(count = exp)
y <- dat %>%
dplyr::select(c("sample","gt","sex", "obs","total")) %>%
mutate(type = "obs") %>%
dplyr::rename(count = obs) %>%
mutate(prop = (count/total))
df_male <- rbind(x, y) %>%
dplyr::filter(sex == "male")
df_fem <- rbind(x, y) %>%
dplyr::filter(sex == "female")
p_male_pooled <- ggplot(df_male, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("F2 males") +
ylab("Proportion of F2 genotype") +
labs(title = "F2 Males of F1 male (1/1) x female (0/0)",
subtitle = paste0("Pooled sites = ", df_male %>% dplyr::filter(gt == "0/0") %>% dplyr::filter(type == "obs") %>% summarise(sum(total)) %>% as.numeric())) +
scale_x_discrete("type", labels = c("Expected","Observed")) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_text(size=15),
axis.title.x = element_blank())
p_female_pooled <- ggplot(df_fem, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("F2 females") +
ylab("Proportion of F2 genotype") +
labs(title = "F2 females of F1 male (1/1) x female (0/0)",
subtitle = paste0("Pooled sites = ", df_fem %>% dplyr::filter(gt == "0/0") %>% dplyr::filter(type == "obs") %>% summarise(sum(total)) %>% as.numeric()))+
scale_x_discrete("type", labels = c("Expected","Observed")) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_text(size=15),
axis.title.x = element_blank())
grid.arrange(p_female_pooled, p_male_pooled, ncol = 2)
ggplot(df_male, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("F2 siblings") +
ylab("Proportion of F2 genotype") +
labs(title = "F2 Males of F1 male (1/1) x female (0/0)") +
# geom_text(aes(label = n), position = position_stack(vjust = 0.5)) +
geom_text(data = filter(df_male, gt == "1/1"), aes(y=prop, x=type, label=total), vjust = 0) +
facet_wrap(~ sample) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_text(size=15),
axis.title.x = element_blank()) 
ggplot(df_fem, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("F2 siblings") +
ylab("Proportion of F2 genotype") +
labs(title = "F2 Females of F1 male (1/1) x female (0/0)") +
# geom_text(aes(label = n), position = position_stack(vjust = 0.5)) +
geom_text(data = filter(df_fem, gt == "1/1"), aes(y=prop, x=type, label=total), vjust = 0) +
facet_wrap(~ sample) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_text(size=15),
axis.title.x = element_blank()) 
F0 foundress
we expect ?????…
# plot the pooled data for all F0 foundress with male (1/1) x female (0/0) offspring
ggplot(observed, aes(fill=gt, y=prop, x = type)) +
geom_bar(position="fill", stat="identity") +
ylab("Proportion of F0 genotype") +
labs(title = "F0 foundress of F1 male (1/1) x female (0/0)",
subtitle = paste0("Pooled sites = ", observed %>% dplyr::filter(gt == "0/1") %>% summarise(sum(total)) %>% as.numeric())) +
theme_classic() +
theme(axis.text.x = element_blank(), axis.title.x = element_blank()) +
labs(fill = "Genotype")
# plot for each individual:
ggplot(observed, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("Observed") +
ylab("Proportion of F0 genotype") +
labs(title = "F0 foundress of F1 male (1/1) x female (0/0)") +
geom_text(data = filter(observed, gt == "1/1"), aes(y=prop, x=type, label=total), vjust = 0) +
facet_wrap(~ sample) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_blank(), axis.title.x = element_blank()) 
Based the ‘Control crossing’ results, I made a vector with all ‘False sites’. These sites will be excluded from the following analyses (5-9).
List of ‘False sites’:
# based on the former control crossing - make a vector of the false sites:
# deduct these sites form the 'table' fileCrossing homozygotic male, with heterozygotic female
(5) F1 male (0/0) x female (0/1)
F2 offspring
We expect F2 females’ sites to be evenly distributed between 0/1 and 0/0 genotypes, and F2 males to be evenly distributed between 0/0 and 1/1 (as they are hemizygote).
# set the families (include only families with at least one adult F2)
family = grep("grndat|grnson",gt$ID, value=TRUE) %>%
str_extract("[^_]+") %>%
unique()
# define a list to put all the data frames in
obs <- list()
for (fam in family) {
obs[[fam]] <- table %>%
dplyr::select(starts_with(fam)) %>%
dplyr::filter_at(vars(matches("_son")), all_vars(. == "0/0")) %>%
dplyr::filter_at(vars(matches("_dat")), all_vars(. == "0/1")) %>%
dplyr::select(contains("grn")) %>%
tidyr::pivot_longer(everything()) %>%
dplyr::rename(sample = name, gt = value) %>%
dplyr::count(sample, gt, .drop = FALSE) %>%
dplyr::filter(gt %in% c("0/0", "1/1", "0/1")) %>%
mutate(n = as.numeric(n)) %>%
group_by(sample) %>%
mutate(total = as.numeric(sum(n))) %>%
dplyr::rename(obs = n) %>%
mutate(sex = case_when(
grepl("son", sample) ~ "male",
grepl("dat", sample) ~ "female"))
}
# bind all families together, to a final data frame containing all observed counts
observed <- do.call("rbind", obs)
# state the expected site proportion of each genotype and sex under a perfect mendelian segregation of F2
fem_exp <- data.frame(sex = "female", gt = c("0/0", "0/1", "1/1") , prop = c(0.5, 0.5,0))
male_exp <- data.frame(sex = "male",gt = c("0/0", "0/1", "1/1"),prop = c(0.5, 0,0.5))
mendel <- bind_rows(fem_exp,male_exp) %>% mutate(sex = as.character(sex))%>% mutate(gt = as.character(gt))
# mutate(type = "exp")
# using on the total genotypes count of sites per sample, calculate the expected counts, and add them to the observed counts into a final dat table
dat <- observed %>%
#mutate(sample = as.character(sample)) %>%
mutate(gt = as.character(gt)) %>%
left_join(mendel, by = c("gt", "sex")) %>%
mutate(exp = total*prop)x <- dat %>%
dplyr::select(c("sample","gt","sex", "exp","prop", "total")) %>%
mutate(type = "exp") %>%
dplyr::rename(count = exp)
y <- dat %>%
dplyr::select(c("sample","gt","sex", "obs","total")) %>%
mutate(type = "obs") %>%
dplyr::rename(count = obs) %>%
mutate(prop = (count/total))
df_male <- rbind(x, y) %>%
dplyr::filter(sex == "male")
df_fem <- rbind(x, y) %>%
dplyr::filter(sex == "female")
p_male_pooled <- ggplot(df_male, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("F2 males") +
ylab("Proportion of F2 genotype") +
labs(title = "F2 Males of F1 male (0/0) x female (0/1)",
subtitle = paste0("Pooled sites = ", df_male %>% dplyr::filter(gt == "0/0") %>% dplyr::filter(type == "obs") %>% summarise(sum(total)) %>% as.numeric())) +
scale_x_discrete("type", labels = c("Expected","Observed")) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_text(size=15),
axis.title.x = element_blank())
p_female_pooled <- ggplot(df_fem, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("F2 females") +
ylab("Proportion of F2 genotype") +
labs(title = "F2 females of F1 male (0/0) x female (0/1)",
subtitle = paste0("Pooled sites = ", df_fem %>% dplyr::filter(gt == "0/0") %>% dplyr::filter(type == "obs") %>% summarise(sum(total)) %>% as.numeric())) +
scale_x_discrete("type", labels = c("Expected","Observed")) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_text(size=15),
axis.title.x = element_blank())
grid.arrange(p_female_pooled, p_male_pooled, ncol = 2)
ggplot(df_male, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("F2 siblings") +
ylab("Proportion of F2 genotype") +
labs(title = "F2 Males of F1 male (0/0) x female (0/1)") +
# geom_text(aes(label = n), position = position_stack(vjust = 0.5)) +
geom_text(data = filter(df_male, gt == "1/1"), aes(y=prop, x=type, label=total), vjust = 0) +
facet_wrap(~ sample) +
labs(fill = "Genotype") +
theme_classic()
ggplot(df_fem, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("F2 siblings") +
ylab("Proportion of F2 genotype") +
labs(title = "F2 Females of F1 male (0/0) x female (0/1)") +
# geom_text(aes(label = n), position = position_stack(vjust = 0.5)) +
geom_text(data = filter(df_fem, gt == "1/1"), aes(y=prop, x=type, label=total), vjust = 0) +
facet_wrap(~ sample) +
labs(fill = "Genotype") +
theme_classic()
F0 foundress
we expect ?????…
# plot the pooled data for all F0 foundress with male (0/0) x female (0/1) offspring
ggplot(observed, aes(fill=gt, y=prop, x = type)) +
geom_bar(position="fill", stat="identity") +
ylab("Proportion of F0 genotype") +
labs(title = "F0 foundress of F1 male (0/0) x female (0/1)",
subtitle = paste0("Pooled sites = ", observed %>% dplyr::filter(gt == "0/1") %>% summarise(sum(total)) %>% as.numeric())) +
theme_classic() +
theme(axis.text.x = element_blank(), axis.title.x = element_blank()) +
labs(fill = "Genotype")
# plot for each individual:
ggplot(observed, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("Observed") +
ylab("Proportion of F0 genotype") +
labs(title = "F0 foundress of F1 male (0/0) x female (0/1)") +
geom_text(data = filter(observed, gt == "1/1"), aes(y=prop, x=type, label=total), vjust = 0) +
facet_wrap(~ sample) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_blank(), axis.title.x = element_blank()) 
Based the ‘Control crossing’ results, I made a vector with all ‘False sites’. These sites will be excluded from the following analyses (5-9).
(6) F1 male (1/1) x female (0/1)
F2 offpsring
We expect F2 females’ sites to be evenly distributed between 0/1 and 1/1 genotypes, and F2 males to be evenly distributed between 0/0 and 1/1 (as they are hemizygote).
# set the families (include only families with at least one adult F2)
family = grep("grndat|grnson",gt$ID, value=TRUE) %>%
str_extract("[^_]+") %>%
unique()
# define a list to put all the data frames in
obs <- list()
for (fam in family) {
obs[[fam]] <- table %>%
dplyr::select(starts_with(fam)) %>%
dplyr::filter_at(vars(matches("_son")), all_vars(. == "1/1")) %>%
dplyr::filter_at(vars(matches("_dat")), all_vars(. == "0/1")) %>%
dplyr::select(contains("grn")) %>%
tidyr::pivot_longer(everything()) %>%
dplyr::rename(sample = name, gt = value) %>%
dplyr::count(sample, gt, .drop = FALSE) %>%
dplyr::filter(gt %in% c("0/0", "1/1", "0/1")) %>%
mutate(n = as.numeric(n)) %>%
group_by(sample) %>%
mutate(total = as.numeric(sum(n))) %>%
dplyr::rename(obs = n) %>%
mutate(sex = case_when(
grepl("son", sample) ~ "male",
grepl("dat", sample) ~ "female"))
}
# bind all families together, to a final data frame containing all observed counts
observed <- do.call("rbind", obs)
# state the expected site proportion of each genotype and sex under a perfect mendelian segregation of F2
fem_exp <- data.frame(sex = "female", gt = c("0/0", "0/1", "1/1") , prop = c(0,0.5,1))
male_exp <- data.frame(sex = "male",gt = c("0/0", "0/1", "1/1"),prop = c(0.5,0,0.5))
mendel <- bind_rows(fem_exp,male_exp) %>% mutate(sex = as.character(sex))%>% mutate(gt = as.character(gt))
# mutate(type = "exp")
# using on the total genotypes count of sites per sample, calculate the expected counts, and add them to the observed counts into a final dat table
dat <- observed %>%
#mutate(sample = as.character(sample)) %>%
mutate(gt = as.character(gt)) %>%
left_join(mendel, by = c("gt", "sex")) %>%
mutate(exp = total*prop)x <- dat %>%
dplyr::select(c("sample","gt","sex", "exp","prop", "total")) %>%
mutate(type = "exp") %>%
dplyr::rename(count = exp)
y <- dat %>%
dplyr::select(c("sample","gt","sex", "obs","total")) %>%
mutate(type = "obs") %>%
dplyr::rename(count = obs) %>%
mutate(prop = (count/total))
df_male <- rbind(x, y) %>%
dplyr::filter(sex == "male")
df_fem <- rbind(x, y) %>%
dplyr::filter(sex == "female")
p_male_pooled <- ggplot(df_male, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("F2 males") +
ylab("Proportion of F2 genotype") +
labs(title = "F2 Males of F1 male (1/1) x female (0/1)",
subtitle = paste0("Pooled sites = ", df_male %>% dplyr::filter(gt == "0/0") %>% dplyr::filter(type == "obs") %>% summarise(sum(total)) %>% as.numeric())) +
scale_x_discrete("type", labels = c("Expected","Observed")) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_text(size=15),
axis.title.x = element_blank())
p_female_pooled <- ggplot(df_fem, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("F2 females") +
ylab("Proportion of F2 genotype") +
labs(title = "F2 females of F1 male (1/1) x female (0/1)",
subtitle = paste0("Pooled sites = ", df_fem %>% dplyr::filter(gt == "0/0") %>% dplyr::filter(type == "obs") %>% summarise(sum(total)) %>% as.numeric())) +
scale_x_discrete("type", labels = c("Expected","Observed")) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_text(size=15),
axis.title.x = element_blank())
grid.arrange(p_female_pooled, p_male_pooled, ncol = 2)
ggplot(df_male, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("F2 siblings") +
ylab("Proportion of F2 genotype") +
labs(title = "F2 Males of F1 male (1/1) x female (0/1)") +
# geom_text(aes(label = n), position = position_stack(vjust = 0.5)) +
geom_text(data = filter(df_male, gt == "1/1"), aes(y=prop, x=type, label=total), vjust = 0) +
facet_wrap(~ sample) +
labs(fill = "Genotype") +
theme_classic()
ggplot(df_fem, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("F2 siblings") +
ylab("Proportion of F2 genotype") +
labs(title = "F2 Females of F1 male (1/1) x female (0/1)") +
# geom_text(aes(label = n), position = position_stack(vjust = 0.5)) +
geom_text(data = filter(df_fem, gt == "1/1"), aes(y=prop, x=type, label=total), vjust = 0) +
facet_wrap(~ sample) +
labs(fill = "Genotype") +
theme_classic()
F0 foundress
we expect ?????…
# plot the pooled data for all F0 foundress with male (1/1) x female (0/1) offspring
ggplot(observed, aes(fill=gt, y=prop, x = type)) +
geom_bar(position="fill", stat="identity") +
ylab("Proportion of F0 genotype") +
labs(title = "F0 foundress of F1 male (1/1) x female (0/1)",
subtitle = paste0("Pooled sites = ", observed %>% dplyr::filter(gt == "0/1") %>% summarise(sum(total)) %>% as.numeric())) +
theme_classic() +
theme(axis.text.x = element_blank(), axis.title.x = element_blank()) +
labs(fill = "Genotype")
# plot for each individual:
ggplot(observed, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("Observed") +
ylab("Proportion of F0 genotype") +
labs(title = "F0 foundress of F1 male (1/1) x female (0/1)") +
geom_text(data = filter(observed, gt == "1/1"), aes(y=prop, x=type, label=total), vjust = 0) +
facet_wrap(~ sample) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_blank(), axis.title.x = element_blank()) 
Based the ‘Control crossing’ results, I made a vector with all ‘False sites’. These sites will be excluded from the following analyses (5-9).
Crossing heterozygotic male, with homozygotic female
The former crosses of heterozygotic females (5 and 6) show that F2
males can be heterozygotic and carry two alleles, like their
mother.
But are these sites real? and if they are, can they be
transmitted to their daughters?
Looking at the F2 offspring of crossing heterozygotic F1 males with
females, we tested 3 possible predictions:
- Heterozygotic sites are false: they are actually hemizygotic (0)
- Heterozygotic sites are false: they are actually hemizygotic (1)
- Heterozygotic sites are real: Males can carry and inherit two alleles (0/1)
(7) F1 male (0/1) x female (0/0)
F2 offspring
# set the families (include only families with at least one adult F2)
family = grep("grndat|grnson",gt$ID, value=TRUE) %>%
str_extract("[^_]+") %>%
unique()
# define a list to put all the data frames in
obs <- list()
for (fam in family) {
obs[[fam]] <- table %>%
dplyr::select(starts_with(fam)) %>%
dplyr::filter_at(vars(matches("_son")), all_vars(. == "0/1")) %>%
dplyr::filter_at(vars(matches("_dat")), all_vars(. == "0/0")) %>%
dplyr::select(contains("grn")) %>%
tidyr::pivot_longer(everything()) %>%
dplyr::rename(sample = name, gt = value) %>%
dplyr::count(sample, gt, .drop = FALSE) %>%
dplyr::filter(gt %in% c("0/0", "1/1", "0/1")) %>%
mutate(n = as.numeric(n)) %>%
group_by(sample) %>%
mutate(total = as.numeric(sum(n))) %>%
dplyr::rename(obs = n) %>%
mutate(sex = case_when(
grepl("son", sample) ~ "male",
grepl("dat", sample) ~ "female"))
}
# bind all families together, to a final data frame containing all observed counts
observed <- do.call("rbind", obs)
# state the expected site proportion of each genotype and sex under the different hypothesis:
# Heterozygotic sites are real:Males can carry and inherit two alleles
fem_sitesReal <- data.frame(type = "sitesReal", sex = "female", gt = c("0/0", "0/1", "1/1") , prop = c(0.5, 0.5, 0))
# Heterozygotic sites are false:Males can carry and inherit only one allele
# the heterozygotic sites are actually (0)
fem_sitesFalse_0 <- data.frame(type = "sitesFalse_0",sex = "female", gt = c("0/0", "0/1", "1/1") , prop = c(1,0,0))
# the heterozygotic sites are actually (1)
fem_sitesFalse_1 <- data.frame(type = "sitesFalse_1", sex = "female", gt = c("0/0", "0/1", "1/1") , prop = c(0,1,0))
# males F2 genotype is expected to stay the same , as it is produced from unfertilized egg, hence have no paternal inheritance
male_sitesReal <- data.frame(type = "sitesReal",sex = "male",gt = c("0/0", "0/1", "1/1"),prop = c(1,0,0))
male_sitesFalse_0 <- data.frame(type = "sitesFalse_0",sex = "male",gt = c("0/0", "0/1", "1/1"),prop = c(1,0,0))
male_sitesFalse_1 <- data.frame(type = "sitesFalse_1", sex = "male",gt = c("0/0", "0/1", "1/1"),prop = c(1,0,0))
expected_prop <- bind_rows(fem_sitesReal,fem_sitesFalse_0,fem_sitesFalse_1,male_sitesReal,male_sitesFalse_0,male_sitesFalse_1) %>% mutate(sex = as.character(sex))%>% mutate(gt = as.character(gt))
# mutate(type = "exp")
# using on the total genotypes count of sites per sample, calculate the expected counts, and add them to the observed counts into a final dat table
expected <- observed %>%
mutate(gt = as.character(gt)) %>%
full_join(expected_prop, by = c("gt", "sex")) %>%
mutate(count = total*prop)
x <- observed %>%
dplyr::select(c("sample","sex","gt","total", "obs")) %>%
mutate(type = "Observed") %>%
dplyr::rename(count = obs) %>%
mutate(prop = (count/total))
dat <- bind_rows(x, expected) %>%
dplyr::select(-obs)# subset for males and females, and reorder the "types" levels:
dat_fem <- dat %>% dplyr::filter(sex == "female")
level_order_fem <- factor(dat_fem$type, level = c("sitesFalse_0", "sitesFalse_1", "sitesReal", "Observed"))
dat_male <- dat %>% dplyr::filter(sex == "male")
level_order_male <- factor(dat_male$type, level = c("sitesFalse_0", "sitesFalse_1", "sitesReal", "Observed"))
p_male_pooled <- ggplot(dat_male, aes(fill=gt, y=prop, x=level_order_male)) +
geom_bar(position="fill", stat="identity", width = 0.8) +
ylab("Proportion of F2 genotype") +
labs(title = paste0("F2 Male, pooled sites = ", dat_male %>% dplyr::filter(type == "Observed") %>% summarise(sum(count)) %>% as.numeric())) +
labs(fill = "Genotype:") +
theme_classic() +
theme(axis.text.x = element_text(size=12, angle = 45,hjust = 1),
axis.title.x = element_blank())
p_female_pooled <- ggplot(dat_fem, aes(fill=gt, y=prop, x=level_order_fem)) +
geom_bar(position="fill", stat="identity", width = 0.8) +
ylab("Proportion of F2 genotype") +
labs(title = paste0("F2 Female, pooled sites = ", dat_fem %>% dplyr::filter(type == "Observed") %>% summarise(sum(count)) %>% as.numeric())) +
labs(fill = "Genotype:") +
theme_classic() +
theme(axis.text.x = element_text(size=12, angle = 45,hjust = 1),
axis.title.x = element_blank())
combined <- p_female_pooled + p_male_pooled & theme(legend.position = "bottom")
combined + plot_layout(guides = "collect") + plot_annotation(
title = 'F2 genotype. F1 Cross: male (0/1) x female (0/0)')
# plot each individual:
ggplot(dat_male, aes(fill=gt, y=prop, x=level_order_male)) +
geom_bar(position="fill", stat="identity", ) +
ylab("Proportion of F2 genotype") +
labs(title = "F2 male genotype. F1 Cross: male (0/1) x female (0/0)", fill = "Genotype:") +
geom_text(data = dat_male %>% filter(gt == "1/1") %>% filter(type == "Observed"), aes(x = 4, y = 0.8, label = total), inherit.aes = FALSE) +
facet_wrap(~ sample) +
theme_classic()+
theme(legend.position = "bottom", axis.title.x = element_blank(), axis.text.x = element_text(size=8, angle = 45,hjust = 1)) 
ggplot(dat_fem, aes(fill=gt, y=prop, x=level_order_fem)) +
geom_bar(position="fill", stat="identity", ) +
ylab("Proportion of F2 genotype") +
labs(title = "F2 female genotype. F1 Cross: male (0/1) x female (0/0)", fill = "Genotype:") +
geom_text(data = dat_fem %>% filter(gt == "1/1") %>% filter(type == "Observed"), aes(x = 4, y = 0.8, label = total), inherit.aes = FALSE) +
facet_wrap(~ sample) +
theme_classic()+
theme(legend.position = "bottom", axis.title.x = element_blank(), axis.text.x = element_text(size=8, angle = 45,hjust = 1)) 
F0 foundress
we expect ?????…
# plot the pooled data for all F0 foundress with male (0/1) x female (0/0) offspring
ggplot(observed, aes(fill=gt, y=prop, x = type)) +
geom_bar(position="fill", stat="identity") +
ylab("Proportion of F0 genotype") +
labs(title = "F0 foundress of F1 male (0/1) x female (0/0)",
subtitle = paste0("Pooled sites = ", observed %>% dplyr::filter(gt == "0/1") %>% summarise(sum(total)) %>% as.numeric())) +
theme_classic() +
theme(axis.text.x = element_blank(), axis.title.x = element_blank()) +
labs(fill = "Genotype")
# plot for each individual:
ggplot(observed, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("Observed") +
ylab("Proportion of F0 genotype") +
labs(title = "F0 foundress of F1 male (0/1) x female (0/0)") +
geom_text(data = filter(observed, gt == "1/1"), aes(y=prop, x=type, label=total), vjust = 0) +
facet_wrap(~ sample) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_blank(), axis.title.x = element_blank()) 
Based the ‘Control crossing’ results, I made a vector with all ‘False sites’. These sites will be excluded from the following analyses (5-9).
(8) F1 male (0/1) x female (1/1)
F2 offspring
# set the families (include only families with at least one adult F2)
family = grep("grndat|grnson",gt$ID, value=TRUE) %>%
str_extract("[^_]+") %>%
unique()
# define a list to put all the data frames in
obs <- list()
for (fam in family) {
obs[[fam]] <- table %>%
dplyr::select(starts_with(fam)) %>%
dplyr::filter_at(vars(matches("_son")), all_vars(. == "0/1")) %>%
dplyr::filter_at(vars(matches("_dat")), all_vars(. == "1/1")) %>%
dplyr::select(contains("grn")) %>%
tidyr::pivot_longer(everything()) %>%
dplyr::rename(sample = name, gt = value) %>%
dplyr::count(sample, gt, .drop = FALSE) %>%
dplyr::filter(gt %in% c("0/0", "1/1", "0/1")) %>%
mutate(n = as.numeric(n)) %>%
group_by(sample) %>%
mutate(total = as.numeric(sum(n))) %>%
dplyr::rename(obs = n) %>%
mutate(sex = case_when(
grepl("son", sample) ~ "male",
grepl("dat", sample) ~ "female"))
}
# bind all families together, to a final data frame containing all observed counts
observed <- do.call("rbind", obs)
# state the expected site proportion of each genotype and sex under the different hypothesis:
# Heterozygotic sites are real:Males can carry and inherit two alleles
fem_sitesReal <- data.frame(type = "sitesReal", sex = "female", gt = c("0/0", "0/1", "1/1") , prop = c(0,0.5,0.5))
# Heterozygotic sites are false:Males can carry and inherit only one allele
# the heterozygotic sites are actually (0)
fem_sitesFalse_0 <- data.frame(type = "sitesFalse_0",sex = "female", gt = c("0/0", "0/1", "1/1") , prop = c(0,1,0))
# the heterozygotic sites are actually (1)
fem_sitesFalse_1 <- data.frame(type = "sitesFalse_1", sex = "female", gt = c("0/0", "0/1", "1/1") , prop = c(0,0,1))
# males F2 genotype is expected to stay the same , as it is produced from unfertilized egg, hence have no paternal inheritance
male_sitesReal <- data.frame(type = "sitesReal",sex = "male",gt = c("0/0", "0/1", "1/1"),prop = c(0,0,1))
male_sitesFalse_0 <- data.frame(type = "sitesFalse_0",sex = "male",gt = c("0/0", "0/1", "1/1"),prop = c(0,0,1))
male_sitesFalse_1 <- data.frame(type = "sitesFalse_1", sex = "male",gt = c("0/0", "0/1", "1/1"),prop = c(0,0,1))
expected_prop <- bind_rows(fem_sitesReal,fem_sitesFalse_0,fem_sitesFalse_1,male_sitesReal,male_sitesFalse_0,male_sitesFalse_1) %>% mutate(sex = as.character(sex))%>% mutate(gt = as.character(gt))
# mutate(type = "exp")
# using on the total genotypes count of sites per sample, calculate the expected counts, and add them to the observed counts into a final dat table
expected <- observed %>%
mutate(gt = as.character(gt)) %>%
full_join(expected_prop, by = c("gt", "sex")) %>%
mutate(count = total*prop)
x <- observed %>%
dplyr::select(c("sample","sex","gt","total", "obs")) %>%
mutate(type = "Observed") %>%
dplyr::rename(count = obs) %>%
mutate(prop = (count/total))
dat <- bind_rows(x, expected) %>%
dplyr::select(-obs)# subset for males and females, and reorder the "types" levels:
dat_fem <- dat %>% dplyr::filter(sex == "female")
level_order_fem <- factor(dat_fem$type, level = c("sitesFalse_0", "sitesFalse_1", "sitesReal", "Observed"))
dat_male <- dat %>% dplyr::filter(sex == "male")
level_order_male <- factor(dat_male$type, level = c("sitesFalse_0", "sitesFalse_1", "sitesReal", "Observed"))
p_male_pooled <- ggplot(dat_male, aes(fill=gt, y=prop, x=level_order_male)) +
geom_bar(position="fill", stat="identity", width = 0.8) +
ylab("Proportion of F2 genotype") +
labs(title = paste0("F2 Male, pooled sites = ", dat_male %>% dplyr::filter(type == "Observed") %>% summarise(sum(count)) %>% as.numeric())) +
labs(fill = "Genotype:") +
theme_classic() +
theme(axis.text.x = element_text(size=12, angle = 45,hjust = 1),
axis.title.x = element_blank())
p_female_pooled <- ggplot(dat_fem, aes(fill=gt, y=prop, x=level_order_fem)) +
geom_bar(position="fill", stat="identity", width = 0.8) +
ylab("Proportion of F2 genotype") +
labs(title = paste0("F2 Female, pooled sites = ", dat_fem %>% dplyr::filter(type == "Observed") %>% summarise(sum(count)) %>% as.numeric())) +
labs(fill = "Genotype:") +
theme_classic() +
theme(axis.text.x = element_text(size=12, angle = 45,hjust = 1),
axis.title.x = element_blank())
combined <- p_female_pooled + p_male_pooled & theme(legend.position = "bottom")
combined + plot_layout(guides = "collect") + plot_annotation(
title = 'F2 genotype. F1 Cross: male (0/1) x female (1/1)')
# plot each individual:
ggplot(dat_male, aes(fill=gt, y=prop, x=level_order_male)) +
geom_bar(position="fill", stat="identity", ) +
ylab("Proportion of F2 genotype") +
labs(title = "F2 male genotype. F1 Cross: male (0/1) x female (1/1)", fill = "Genotype:") +
geom_text(data = dat_male %>% filter(gt == "1/1") %>% filter(type == "Observed"), aes(x = 4, y = 0.8, label = total), inherit.aes = FALSE) +
facet_wrap(~ sample) +
theme_classic()+
theme(legend.position = "bottom", axis.title.x = element_blank(), axis.text.x = element_text(size=8, angle = 45,hjust = 1)) 
ggplot(dat_fem, aes(fill=gt, y=prop, x=level_order_fem)) +
geom_bar(position="fill", stat="identity", ) +
ylab("Proportion of F2 genotype") +
labs(title = "F2 female genotype. F1 Cross: male (0/1) x female (1/1)", fill = "Genotype:") +
geom_text(data = dat_fem %>% filter(gt == "1/1") %>% filter(type == "Observed"), aes(x = 4, y = 0.8, label = total), inherit.aes = FALSE) +
facet_wrap(~ sample) +
theme_classic()+
theme(legend.position = "bottom", axis.title.x = element_blank(), axis.text.x = element_text(size=8, angle = 45,hjust = 1)) 
F0 foundress
we expect ?????…
# plot the pooled data for all F0 foundress with male (0/1) x female (1/1) offspring
ggplot(observed, aes(fill=gt, y=prop, x = type)) +
geom_bar(position="fill", stat="identity") +
ylab("Proportion of F0 genotype") +
labs(title = "F0 foundress of F1 male (0/1) x female (1/1)",
subtitle = paste0("Pooled sites = ", observed %>% dplyr::filter(gt == "0/1") %>% summarise(sum(total)) %>% as.numeric())) +
theme_classic() +
theme(axis.text.x = element_blank(), axis.title.x = element_blank()) +
labs(fill = "Genotype")
# plot for each individual:
ggplot(observed, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("Observed") +
ylab("Proportion of F0 genotype") +
labs(title = "F0 foundress of F1 male (0/1) x female (1/1)") +
geom_text(data = filter(observed, gt == "1/1"), aes(y=prop, x=type, label=total), vjust = 0) +
facet_wrap(~ sample) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_blank(), axis.title.x = element_blank()) 
Based the ‘Control crossing’ results, I made a vector with all ‘False sites’. These sites will be excluded from the following analyses (5-9).
(9) F1 male (0/1) x female (0/1)
F2 offspring
# set the families (include only families with at least one adult F2)
family = grep("grndat|grnson",gt$ID, value=TRUE) %>%
str_extract("[^_]+") %>%
unique()
# define a list to put all the data frames in
obs <- list()
for (fam in family) {
obs[[fam]] <- table %>%
dplyr::select(starts_with(fam)) %>%
dplyr::filter_at(vars(matches("_son")), all_vars(. == "0/1")) %>%
dplyr::filter_at(vars(matches("_dat")), all_vars(. == "0/1")) %>%
dplyr::select(contains("grn")) %>%
tidyr::pivot_longer(everything()) %>%
dplyr::rename(sample = name, gt = value) %>%
dplyr::count(sample, gt, .drop = FALSE) %>%
dplyr::filter(gt %in% c("0/0", "1/1", "0/1")) %>%
mutate(n = as.numeric(n)) %>%
group_by(sample) %>%
mutate(total = as.numeric(sum(n))) %>%
dplyr::rename(obs = n) %>%
mutate(sex = case_when(
grepl("son", sample) ~ "male",
grepl("dat", sample) ~ "female"))
}
# bind all families together, to a final data frame containing all observed counts
observed <- do.call("rbind", obs)
# state the expected site proportion of each genotype and sex under the different hypothesis:
# Heterozygotic sites are real:Males can carry and inherit two alleles
fem_sitesReal <- data.frame(type = "sitesReal", sex = "female", gt = c("0/0", "0/1", "1/1") , prop = c(0.25, 0.5, 0.25))
# Heterozygotic sites are false:Males can carry and inherit only one allele
# the heterozygotic sites are actually (0)
fem_sitesFalse_0 <- data.frame(type = "sitesFalse_0",sex = "female", gt = c("0/0", "0/1", "1/1") , prop = c(0.5, 0.5, 0))
# the heterozygotic sites are actually (1)
fem_sitesFalse_1 <- data.frame(type = "sitesFalse_1", sex = "female", gt = c("0/0", "0/1", "1/1") , prop = c(0, 0.5, 0.5))
# males F2 genotype is expected to stay the same , as it is produced from unfertilized egg, hence have no paternal inheritance
male_sitesReal <- data.frame(type = "sitesReal",sex = "male",gt = c("0/0", "0/1", "1/1"),prop = c(0.5, 0, 0.5))
male_sitesFalse_0 <- data.frame(type = "sitesFalse_0",sex = "male",gt = c("0/0", "0/1", "1/1"),prop = c(0.5, 0, 0.5))
male_sitesFalse_1 <- data.frame(type = "sitesFalse_1", sex = "male",gt = c("0/0", "0/1", "1/1"),prop = c(0.5, 0, 0.5))
expected_prop <- bind_rows(fem_sitesReal,fem_sitesFalse_0,fem_sitesFalse_1,male_sitesReal,male_sitesFalse_0,male_sitesFalse_1) %>% mutate(sex = as.character(sex))%>% mutate(gt = as.character(gt))
# mutate(type = "exp")
# using on the total genotypes count of sites per sample, calculate the expected counts, and add them to the observed counts into a final dat table
expected <- observed %>%
mutate(gt = as.character(gt)) %>%
full_join(expected_prop, by = c("gt", "sex")) %>%
mutate(count = total*prop)
x <- observed %>%
dplyr::select(c("sample","sex","gt","total", "obs")) %>%
mutate(type = "Observed") %>%
dplyr::rename(count = obs) %>%
mutate(prop = (count/total))
dat <- bind_rows(x, expected) %>%
dplyr::select(-obs)# subset for males and females, and reorder the "types" levels:
dat_fem <- dat %>% dplyr::filter(sex == "female")
level_order_fem <- factor(dat_fem$type, level = c("sitesFalse_0", "sitesFalse_1", "sitesReal", "Observed"))
dat_male <- dat %>% dplyr::filter(sex == "male")
level_order_male <- factor(dat_male$type, level = c("sitesFalse_0", "sitesFalse_1", "sitesReal", "Observed"))
p_male_pooled <- ggplot(dat_male, aes(fill=gt, y=prop, x=level_order_male)) +
geom_bar(position="fill", stat="identity", width = 0.8) +
ylab("Proportion of F2 genotype") +
labs(title = paste0("F2 Male, pooled sites = ", dat_male %>% dplyr::filter(type == "Observed") %>% summarise(sum(count)) %>% as.numeric())) +
labs(fill = "Genotype:") +
theme_classic() +
theme(axis.text.x = element_text(size=12, angle = 45,hjust = 1),
axis.title.x = element_blank())
p_female_pooled <- ggplot(dat_fem, aes(fill=gt, y=prop, x=level_order_fem)) +
geom_bar(position="fill", stat="identity", width = 0.8) +
ylab("Proportion of F2 genotype") +
labs(title = paste0("F2 Female, pooled sites = ", dat_fem %>% dplyr::filter(type == "Observed") %>% summarise(sum(count)) %>% as.numeric())) +
labs(fill = "Genotype:") +
theme_classic() +
theme(axis.text.x = element_text(size=12, angle = 45,hjust = 1),
axis.title.x = element_blank())
combined <- p_female_pooled + p_male_pooled & theme(legend.position = "bottom")
combined + plot_layout(guides = "collect") + plot_annotation(
title = 'F2 genotype. F1 Cross: male (0/1) x female (0/1)')
# plot each individual:
ggplot(dat_male, aes(fill=gt, y=prop, x=level_order_male)) +
geom_bar(position="fill", stat="identity", ) +
ylab("Proportion of F2 genotype") +
labs(title = "F2 male genotype. F1 Cross: male (0/1) x female (0/1)", fill = "Genotype:") +
geom_text(data = dat_male %>% filter(gt == "1/1") %>% filter(type == "Observed"), aes(x = 4, y = 0.8, label = total), inherit.aes = FALSE) +
facet_wrap(~ sample) +
theme_classic()+
theme(legend.position = "bottom", axis.title.x = element_blank(), axis.text.x = element_text(size=8, angle = 45,hjust = 1)) 
ggplot(dat_fem, aes(fill=gt, y=prop, x=level_order_fem)) +
geom_bar(position="fill", stat="identity", ) +
ylab("Proportion of F2 genotype") +
labs(title = "F2 female genotype. F1 Cross: male (0/1) x female (0/1)", fill = "Genotype:") +
geom_text(data = dat_fem %>% filter(gt == "1/1") %>% filter(type == "Observed"), aes(x = 4, y = 0.8, label = total), inherit.aes = FALSE) +
facet_wrap(~ sample) +
theme_classic()+
theme(legend.position = "bottom", axis.title.x = element_blank(), axis.text.x = element_text(size=8, angle = 45,hjust = 1)) 
F0 foundress
we expect ?????…
# plot the pooled data for all F0 foundress with male (0/1) x female (1/1) offspring
ggplot(observed, aes(fill=gt, y=prop, x = type)) +
geom_bar(position="fill", stat="identity") +
ylab("Proportion of F0 genotype") +
labs(title = "F0 foundress of F1 male (0/1) x female (0/1)",
subtitle = paste0("Pooled sites = ", observed %>% dplyr::filter(gt == "1/1") %>% summarise(sum(total)) %>% as.numeric())) +
theme_classic() +
theme(axis.text.x = element_blank(), axis.title.x = element_blank()) +
labs(fill = "Genotype")
# plot for each individual:
ggplot(observed, aes(fill=gt, y=prop, x=type)) +
geom_bar(position="fill", stat="identity", ) +
xlab("Observed") +
ylab("Proportion of F0 genotype") +
labs(title = "F0 foundress of F1 male (0/1) x female (0/1)") +
geom_text(data = filter(observed, gt == "1/1"), aes(y=prop, x=type, label=total), vjust = 0) +
facet_wrap(~ sample) +
labs(fill = "Genotype") +
theme_classic() +
theme(axis.text.x = element_blank(), axis.title.x = element_blank()) 
Statistics
We did 2 types of a-parametric tests for independence, testing the predicted counts against the observed counts:
- Cochran–Mantel–Haenszel Test (CMH) for Repeated Tests of Independence
- Replicated G-test of goodness of fit
Cochran–Mantel–Haenszel Test for Repeated Tests of Independence
CMH Test for females
CMH Test for males
Replicated G-test of goodness of fit
רק לדוגמא
for one family, 284
for one family, 338
for one family, 110
all genotypes for the 3 families
old code for false sites
# set the families (include only families with at least one adult F2)
family = grep("grndat|grnson",gt$ID, value=TRUE) %>%
str_extract("[^_]+") %>%
unique()
# define a list to put all the data frames in
list_grndat <- list()
for (fam in family) {
list_grndat[[fam]] <- table %>%
dplyr::select(starts_with(fam)) %>%
dplyr::filter_at(vars(matches("_son")), all_vars(. == "0/0")) %>%
dplyr::filter_at(vars(matches("_dat")), all_vars(. == "0/0")) %>%
dplyr::select(contains("grndat")) %>%
rownames_to_column("site")
}
#%>% dplyr::filter(across(everything(), ~ !str_detect(., "0/0")))%>% na.omit()
list_grndat$`240` %>% view()
# for male F2
list_grnson <- list()
for (fam in family) {
list_grnson[[fam]] <- table %>%
dplyr::select(starts_with(fam)) %>%
dplyr::filter_at(vars(matches("_son")), all_vars(. == "0/0")) %>%
dplyr::filter_at(vars(matches("_dat")), all_vars(. == "0/0")) %>%
dplyr::select(contains("grnson")) %>%
rownames_to_column("site")
}
#%>% dplyr::filter(across(everything(), ~ !str_detect(., "0/0"))) %>% na.omit()
list_grnson$`600`## site 600_601a_grnson
## 1 NW_019211454.1_4912263 0/0
## 2 NW_019211454.1_4920532 0/0
## 3 NW_019211454.1_4921004 0/0
## 4 NW_019211454.1_4952477 0/0
## 5 NW_019211454.1_4966874 <NA>
## 6 NW_019211454.1_4975120 <NA>
## 7 NW_019211454.1_4978792 0/0
## 8 NW_019211454.1_4980011 <NA>
## 9 NW_019211454.1_5013515 0/0
## 10 NW_019211454.1_5021396 0/0
## 11 NW_019211454.1_5095835 0/0
## 12 NW_019211454.1_5122236 <NA>
## 13 NW_019211454.1_5166671 <NA>
## 14 NW_019211454.1_5175616 <NA>
## 15 NW_019211454.1_5184381 0/0
## 16 NW_019211454.1_5214324 0/0
## 17 NW_019211454.1_5229776 <NA>
## 18 NW_019211454.1_5257386 0/0
## 19 NW_019211454.1_5261860 <NA>
## 20 NW_019211454.1_5266356 0/0
## 21 NW_019211454.1_5268357 0/0
## 22 NW_019211454.1_5279110 0/0
## 23 NW_019211454.1_5918916 0/0
## 24 NW_019211454.1_6033069 0/0
## 25 NW_019211454.1_6041909 0/0
## 26 NW_019211454.1_6167320 <NA>
## 27 NW_019211454.1_6226168 0/0
## 28 NW_019211454.1_6251092 0/0
## 29 NW_019211454.1_6259775 <NA>
## 30 NW_019211454.1_6366438 <NA>
## 31 NW_019211454.1_6453874 <NA>
## 32 NW_019211454.1_6479391 0/0
## 33 NW_019211454.1_6503569 0/0
## 34 NW_019211454.1_6509668 <NA>
## 35 NW_019211454.1_6512550 0/0
## 36 NW_019211454.1_6512723 <NA>
## 37 NW_019211454.1_6516631 <NA>
## 38 NW_019211454.1_6538442 <NA>
## 39 NW_019211454.1_8203978 <NA>
## 40 NW_019211454.1_9513244 <NA>
## 41 NW_019211454.1_9526241 0/0
## 42 NW_019211454.1_9526415 0/0
## 43 NW_019211454.1_10787954 0/0
## 44 NW_019211454.1_10789989 0/0
## 45 NW_019211454.1_12645630 0/0
## 46 NW_019211454.1_12703153 0/0
## 47 NW_019211454.1_12852621 <NA>
## 48 NW_019211454.1_12860342 <NA>
## 49 NW_019211454.1_12869979 <NA>
## 50 NW_019211454.1_12874184 <NA>
## 51 NW_019211454.1_12889655 <NA>
## 52 NW_019211454.1_12943805 <NA>
## 53 NW_019211454.1_13005638 0/0
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## 1697 NW_019211457.1_40548622 0/0
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## 1704 NW_019211457.1_40690400 0/0
## 1705 NW_019211457.1_40706014 0/0
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## 1710 NW_019211457.1_43056390 0/0
## 1711 NW_019211457.1_43062236 0/0
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## 1724 NW_019211457.1_43460214 0/0
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## 1733 NW_019211457.1_43524965 0/0
## 1734 NW_019211457.1_43560657 0/0
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## 1755 NW_019211457.1_43769507 0/0
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## 1759 NW_019211457.1_43784201 0/0
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## 1766 NW_019211457.1_43865442 0/0
## 1767 NW_019211457.1_43866548 0/0
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## 1776 NW_019211457.1_43961122 0/0
## 1777 NW_019211457.1_43988546 0/0
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## 1781 NW_019211457.1_44111224 0/0
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## 1785 NW_019211457.1_44142885 <NA>
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## 1787 NW_019211457.1_44178254 0/0
## 1788 NW_019211457.1_44200136 0/0
## 1789 NW_019211457.1_44235626 0/0
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## 1791 NW_019211457.1_44255839 0/0
## 1792 NW_019211457.1_44275899 0/0
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## 1796 NW_019211457.1_45034118 0/0
## 1797 NW_019211457.1_45035398 <NA>
## 1798 NW_019211457.1_45042444 0/0
## 1799 NW_019211457.1_45048100 0/0
## 1800 NW_019211457.1_45063030 0/0
## 1801 NW_019211457.1_45071546 <NA>
## 1802 NW_019211457.1_45073516 <NA>
## 1803 NW_019211457.1_50318127 0/0
## 1804 NW_019211457.1_50962362 <NA>
## 1805 NW_019211457.1_51443167 0/0
## 1806 NW_019211457.1_51478363 0/0
## 1807 NW_019211457.1_51513257 0/0
## 1808 NW_019211457.1_51546372 <NA>
## 1809 NW_019211457.1_51581192 <NA>
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## 1811 NW_019211457.1_51588475 0/0
## 1812 NW_019211457.1_51683102 0/0
## 1813 NW_019211457.1_51705140 <NA>
## 1814 NW_019211457.1_51710311 0/0
## 1815 NW_019211457.1_51761327 0/0
## 1816 NW_019211457.1_51763547 0/0
## 1817 NW_019211457.1_51769826 <NA>
## 1818 NW_019211457.1_51775782 0/0
## 1819 NW_019211457.1_51779339 0/0
## 1820 NW_019211457.1_51873098 0/0
## 1821 NW_019211457.1_51944703 0/0
## 1822 NW_019211457.1_51961139 <NA>
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## 1824 NW_019211457.1_51976089 0/0
## 1825 NW_019211457.1_52045948 0/0
## 1826 NW_019211457.1_52069480 0/0
## 1827 NW_019211457.1_52104315 <NA>
## 1828 NW_019211457.1_52183824 <NA>
## 1829 NW_019211457.1_52184827 0/0
## 1830 NW_019211457.1_52196943 <NA>
## 1831 NW_019211457.1_52249527 <NA>
## 1832 NW_019211457.1_52258187 0/0
## 1833 NW_019211457.1_52302222 <NA>
## 1834 NW_019211457.1_52321663 0/0
## 1835 NW_019211457.1_52353812 0/0
## 1836 NW_019211457.1_52363162 <NA>
## 1837 NW_019211457.1_52365285 0/0
## 1838 NW_019211457.1_52365614 0/0
## 1839 NW_019211457.1_52387359 0/0
## 1840 NW_019211457.1_52402281 <NA>
## 1841 NW_019211457.1_52404236 0/0
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## 1843 NW_019211457.1_52417275 0/0
## 1844 NW_019211457.1_52417534 0/0
## 1845 NW_019211457.1_52443043 <NA>
## 1846 NW_019211457.1_52454244 <NA>
## 1847 NW_019211457.1_52469489 0/0
## 1848 NW_019211457.1_52478758 0/0
## 1849 NW_019211457.1_52478780 0/0
## 1850 NW_019211457.1_52479560 0/0
## 1851 NW_019211457.1_52479570 0/0
## 1852 NW_019211457.1_52482140 0/0
## 1853 NW_019211457.1_52661003 0/0
## 1854 NW_019211458.1_1298382 <NA>
## 1855 NW_019211458.1_1583182 <NA>
## 1856 NW_019211458.1_2084304 0/0
## 1857 NW_019211458.1_2563007 <NA>
## 1858 NW_019211458.1_2912793 <NA>
## 1859 NW_019211458.1_3486581 0/0
## 1860 NW_019211458.1_3501170 0/0
## 1861 NW_019211458.1_3517571 0/0
## 1862 NW_019211458.1_3538441 0/0
## 1863 NW_019211458.1_3563440 0/0
## 1864 NW_019211458.1_3594149 <NA>
## 1865 NW_019211458.1_3603498 <NA>
## 1866 NW_019211458.1_3615957 <NA>
## 1867 NW_019211458.1_3632231 0/0
## 1868 NW_019211458.1_3634791 0/0
## 1869 NW_019211458.1_3637222 <NA>
## 1870 NW_019211458.1_3638327 <NA>
## 1871 NW_019211458.1_3638631 <NA>
## 1872 NW_019211458.1_3639626 <NA>
## 1873 NW_019211458.1_3640124 <NA>
## 1874 NW_019211458.1_3660510 0/0
## 1875 NW_019211458.1_3674364 <NA>
## 1876 NW_019211458.1_3677885 <NA>
## 1877 NW_019211458.1_3682080 0/0
## 1878 NW_019211458.1_3697615 0/0
## 1879 NW_019211458.1_3705441 0/0
## 1880 NW_019211458.1_3717645 <NA>
## 1881 NW_019211458.1_3761906 <NA>
## 1882 NW_019211458.1_3904036 0/0
## 1883 NW_019211458.1_3915474 <NA>
## 1884 NW_019211458.1_3932901 0/0
## 1885 NW_019211458.1_3958477 <NA>
## 1886 NW_019211458.1_3972721 <NA>
## 1887 NW_019211458.1_4030552 <NA>
## 1888 NW_019211458.1_4073158 <NA>
## 1889 NW_019211458.1_4084498 <NA>
## 1890 NW_019211458.1_4147248 0/0
## 1891 NW_019211458.1_4161593 0/0
## 1892 NW_019211458.1_4204532 <NA>
## 1893 NW_019211458.1_4237489 <NA>
## 1894 NW_019211458.1_4259639 0/0
## 1895 NW_019211458.1_4265158 <NA>
## 1896 NW_019211458.1_4321607 0/0
## 1897 NW_019211458.1_4328941 0/0
## 1898 NW_019211458.1_4394285 <NA>
## 1899 NW_019211458.1_4503041 <NA>
## 1900 NW_019211458.1_4537007 0/0
## 1901 NW_019211458.1_4561542 <NA>
## 1902 NW_019211458.1_4562049 <NA>
## 1903 NW_019211458.1_4567805 0/0
## 1904 NW_019211458.1_4599456 <NA>
## 1905 NW_019211458.1_4601710 0/0
## 1906 NW_019211458.1_4718955 <NA>
## 1907 NW_019211458.1_4731554 <NA>
## 1908 NW_019211458.1_4736124 <NA>
## 1909 NW_019211458.1_4767724 <NA>
## 1910 NW_019211458.1_4791848 <NA>
## 1911 NW_019211458.1_4807639 <NA>
## 1912 NW_019211458.1_4853777 <NA>
## 1913 NW_019211458.1_4859954 0/0
## 1914 NW_019211458.1_4864440 0/0
## 1915 NW_019211458.1_4873032 0/0
## 1916 NW_019211458.1_4925166 0/0
## 1917 NW_019211458.1_5039431 <NA>
## 1918 NW_019211458.1_5044338 0/0
## 1919 NW_019211458.1_5063604 1/1
## 1920 NW_019211458.1_5086921 <NA>
## 1921 NW_019211458.1_5110485 0/0
## 1922 NW_019211458.1_5118361 <NA>
## 1923 NW_019211458.1_5128990 <NA>
## 1924 NW_019211458.1_5173802 0/0
## 1925 NW_019211458.1_5192566 0/0
## 1926 NW_019211458.1_5192913 <NA>
## 1927 NW_019211458.1_5199497 0/0
## 1928 NW_019211458.1_5269581 0/0
## 1929 NW_019211458.1_5299536 0/0
## 1930 NW_019211458.1_5299674 <NA>
## 1931 NW_019211458.1_5307804 <NA>
## 1932 NW_019211458.1_5374857 0/0
## 1933 NW_019211458.1_5479501 0/0
## 1934 NW_019211458.1_5494423 <NA>
## 1935 NW_019211458.1_5497967 <NA>
## 1936 NW_019211458.1_5503263 0/0
## 1937 NW_019211458.1_5504618 0/0
## 1938 NW_019211458.1_5511453 0/0
## 1939 NW_019211458.1_5523299 <NA>
## 1940 NW_019211458.1_5531127 <NA>
## 1941 NW_019211458.1_5535808 0/0
## 1942 NW_019211458.1_5546803 0/0
## 1943 NW_019211458.1_5547623 0/0
## 1944 NW_019211458.1_5557778 0/0
## 1945 NW_019211458.1_5574279 0/0
## 1946 NW_019211458.1_5577966 <NA>
## 1947 NW_019211458.1_5595097 <NA>
## 1948 NW_019211458.1_5600497 0/0
## 1949 NW_019211458.1_5600613 <NA>
## 1950 NW_019211458.1_5613882 0/0
## 1951 NW_019211458.1_5631738 <NA>
## 1952 NW_019211458.1_5680791 0/0
## 1953 NW_019211458.1_5694042 <NA>
## 1954 NW_019211458.1_5722833 0/0
## 1955 NW_019211458.1_5741658 0/0
## 1956 NW_019211458.1_5741703 <NA>
## 1957 NW_019211458.1_5804801 <NA>
## 1958 NW_019211458.1_5804897 <NA>
## 1959 NW_019211458.1_5823319 <NA>
## 1960 NW_019211458.1_5847489 <NA>
## 1961 NW_019211458.1_5903748 0/0
## 1962 NW_019211458.1_5912650 <NA>
## 1963 NW_019211458.1_5914918 0/0
## 1964 NW_019211458.1_5917146 0/0
## 1965 NW_019211458.1_5924336 0/0
## 1966 NW_019211458.1_5929745 0/0
## 1967 NW_019211458.1_5934807 <NA>
## 1968 NW_019211458.1_5935652 0/0
## 1969 NW_019211458.1_5937197 0/0
## 1970 NW_019211458.1_5938445 <NA>
## 1971 NW_019211458.1_5938601 0/0
## 1972 NW_019211458.1_5965772 0/0
## 1973 NW_019211458.1_5979869 <NA>
## 1974 NW_019211458.1_5992447 0/0
## 1975 NW_019211458.1_6009470 <NA>
## 1976 NW_019211458.1_6011168 0/0
## 1977 NW_019211458.1_6030794 0/0
## 1978 NW_019211458.1_6042570 <NA>
## 1979 NW_019211458.1_6057177 0/0
## 1980 NW_019211458.1_6058524 0/0
## 1981 NW_019211458.1_6058691 <NA>
## 1982 NW_019211458.1_6061677 0/0
## 1983 NW_019211458.1_6061843 0/0
## 1984 NW_019211458.1_6069829 0/0
## 1985 NW_019211458.1_6069908 0/0
## 1986 NW_019211458.1_6069982 0/0
## 1987 NW_019211458.1_6071819 <NA>
## 1988 NW_019211458.1_6144848 <NA>
## 1989 NW_019211458.1_6151080 <NA>
## 1990 NW_019211458.1_6191019 0/0
## 1991 NW_019211458.1_6191325 0/0
## 1992 NW_019211458.1_6202189 <NA>
## 1993 NW_019211458.1_6223063 <NA>
## 1994 NW_019211458.1_6448227 0/0
## 1995 NW_019211458.1_6565784 <NA>
## 1996 NW_019211458.1_6568869 <NA>
## 1997 NW_019211458.1_6572907 0/0
## 1998 NW_019211458.1_6576809 0/0
## 1999 NW_019211458.1_6598025 0/0
## 2000 NW_019211458.1_6600418 <NA>
## 2001 NW_019211458.1_6619620 <NA>
## 2002 NW_019211458.1_6625453 0/0
## 2003 NW_019211458.1_6640507 <NA>
## 2004 NW_019211458.1_6683613 0/0
## 2005 NW_019211458.1_6751037 0/0
## 2006 NW_019211458.1_6754083 0/0
## 2007 NW_019211458.1_6764061 0/0
## 2008 NW_019211458.1_6764939 <NA>
## 2009 NW_019211458.1_6778749 <NA>
## 2010 NW_019211458.1_6782718 0/0
## 2011 NW_019211458.1_6785629 <NA>
## 2012 NW_019211458.1_6795715 <NA>
## 2013 NW_019211458.1_6803452 <NA>
## 2014 NW_019211458.1_6811442 <NA>
## 2015 NW_019211458.1_6827923 <NA>
## 2016 NW_019211458.1_6842486 0/0
## 2017 NW_019211458.1_6885232 0/0
## 2018 NW_019211458.1_6933656 <NA>
## 2019 NW_019211458.1_6934656 0/0
## 2020 NW_019211458.1_6936668 0/0
## 2021 NW_019211458.1_6961890 <NA>
## 2022 NW_019211458.1_6961981 <NA>
## 2023 NW_019211458.1_6967444 <NA>
## 2024 NW_019211458.1_6980583 0/0
## 2025 NW_019211458.1_6981187 0/0
## 2026 NW_019211458.1_7208511 0/0
## 2027 NW_019211458.1_7235294 <NA>
## 2028 NW_019211458.1_7264863 <NA>
## 2029 NW_019211458.1_7265347 0/0
## 2030 NW_019211458.1_7631653 <NA>
## 2031 NW_019211458.1_7640452 <NA>
## 2032 NW_019211458.1_7774133 0/0
## 2033 NW_019211458.1_7786800 <NA>
## 2034 NW_019211458.1_7787879 0/0
## 2035 NW_019211458.1_7791296 <NA>
## 2036 NW_019211458.1_7799492 0/0
## 2037 NW_019211458.1_7866350 0/0
## 2038 NW_019211458.1_7888320 0/0
## 2039 NW_019211458.1_7889638 0/0
## 2040 NW_019211458.1_7954921 <NA>
## 2041 NW_019211458.1_7974357 0/0
## 2042 NW_019211458.1_7984851 0/0
## 2043 NW_019211458.1_8117696 0/0
## 2044 NW_019211458.1_8237922 <NA>
## 2045 NW_019211458.1_8239891 0/0
## 2046 NW_019211458.1_8251037 <NA>
## 2047 NW_019211458.1_8336887 <NA>
## 2048 NW_019211458.1_8369585 <NA>
## 2049 NW_019211458.1_8409414 0/0
## 2050 NW_019211458.1_8436682 0/0
## 2051 NW_019211458.1_8451711 0/0
## 2052 NW_019211458.1_8466673 0/0
## 2053 NW_019211458.1_8496190 <NA>
## 2054 NW_019211458.1_8504608 <NA>
## 2055 NW_019211458.1_8523779 0/0
## 2056 NW_019211458.1_8541409 <NA>
## 2057 NW_019211458.1_8568317 <NA>
## 2058 NW_019211458.1_8753223 0/0
## 2059 NW_019211458.1_8772588 0/0
## 2060 NW_019211458.1_8801693 0/0
## 2061 NW_019211458.1_8824576 0/0
## 2062 NW_019211458.1_8842277 0/0
## 2063 NW_019211458.1_8845053 <NA>
## 2064 NW_019211458.1_8877896 0/0
## 2065 NW_019211458.1_8887963 0/0
## 2066 NW_019211458.1_8896964 0/0
## 2067 NW_019211458.1_8898831 <NA>
## 2068 NW_019211458.1_8904369 0/0
## 2069 NW_019211458.1_8908098 <NA>
## 2070 NW_019211458.1_8908442 0/0
## 2071 NW_019211458.1_8915801 <NA>
## 2072 NW_019211458.1_8925533 <NA>
## 2073 NW_019211458.1_8929328 <NA>
## 2074 NW_019211458.1_8929373 <NA>
## 2075 NW_019211458.1_8941054 0/0
## 2076 NW_019211458.1_8954838 <NA>
## 2077 NW_019211458.1_11432208 0/0
## 2078 NW_019211458.1_11481565 <NA>
## 2079 NW_019211458.1_11481676 <NA>
## 2080 NW_019211458.1_11482583 0/0
## 2081 NW_019211458.1_11505070 0/0
## 2082 NW_019211458.1_11518442 0/0
## 2083 NW_019211458.1_11519731 0/0
## 2084 NW_019211458.1_11570514 0/0
## 2085 NW_019211458.1_11601340 0/0
## 2086 NW_019211458.1_11713555 <NA>
## 2087 NW_019211458.1_11713592 <NA>
## 2088 NW_019211458.1_11815375 0/1
## 2089 NW_019211458.1_12922078 <NA>
## 2090 NW_019211458.1_14116016 <NA>
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## 2901 NW_019211460.1_22806463 0/0
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## 2909 NW_019211460.1_24053598 0/0
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## 2941 NW_019211460.1_25276669 0/0
## 2942 NW_019211460.1_25278561 0/0
## 2943 NW_019211460.1_25287719 0/0
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## 2946 NW_019211460.1_25307870 0/0
## 2947 NW_019211460.1_25320490 0/0
## 2948 NW_019211460.1_25323653 0/0
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## 2954 NW_019211460.1_25384542 0/0
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## 2958 NW_019211460.1_25433021 0/0
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## 2960 NW_019211460.1_25439872 0/0
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## 2962 NW_019211460.1_25574326 <NA>
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## 2964 NW_019211460.1_25577312 0/0
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## 2967 NW_019211460.1_26171391 0/0
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## 2969 NW_019211460.1_26182516 0/0
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## 2973 NW_019211460.1_26198191 0/0
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## 2976 NW_019211460.1_26235727 0/0
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## 2981 NW_019211460.1_31244709 <NA>
## 2982 NW_019211460.1_31269312 0/0
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## 2984 NW_019211460.1_31320858 0/0
## 2985 NW_019211460.1_31376306 0/0
## 2986 NW_019211460.1_31380075 <NA>
## 2987 NW_019211460.1_31387222 0/0
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## 2989 NW_019211460.1_31389335 0/0
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## 2991 NW_019211460.1_31398725 0/0
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## 2995 NW_019211460.1_31419681 0/0
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## 2998 NW_019211460.1_31427558 0/0
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## 3000 NW_019211460.1_31431478 0/0
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## 3003 NW_019211460.1_31438471 0/0
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## 3006 NW_019211460.1_31489789 0/0
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## 3008 NW_019211460.1_31492247 0/1
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## 3010 NW_019211460.1_31495429 0/0
## 3011 NW_019211460.1_31503669 0/0
## 3012 NW_019211460.1_31503834 0/0
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## 3015 NW_019211460.1_31599116 0/0
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## 3019 NW_019211460.1_31648968 0/0
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## 3022 NW_019211460.1_31675965 0/0
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## 3024 NW_019211460.1_31696355 0/0
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## 3026 NW_019211460.1_31713732 0/0
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## 3028 NW_019211460.1_31722242 0/0
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## 3030 NW_019211460.1_31741624 0/0
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## 3032 NW_019211460.1_32760140 0/0
## 3033 NW_019211460.1_32783801 0/0
## 3034 NW_019211460.1_32785599 <NA>
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## 3036 NW_019211460.1_32791840 <NA>
## 3037 NW_019211460.1_32997494 0/0
## 3038 NW_019211460.1_33010381 0/0
## 3039 NW_019211460.1_33032400 0/0
## 3040 NW_019211460.1_33053490 <NA>
## 3041 NW_019211460.1_33057515 <NA>
## 3042 NW_019211460.1_33060141 0/0
## 3043 NW_019211460.1_33062371 0/0
## 3044 NW_019211460.1_33062504 0/0
## 3045 NW_019211460.1_33062519 0/0
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## 3047 NW_019211460.1_33076181 <NA>
## 3048 NW_019211460.1_33097115 0/0
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## 3051 NW_019211460.1_33304817 0/0
## 3052 NW_019211460.1_33309273 0/0
## 3053 NW_019211460.1_33310506 0/0
## 3054 NW_019211460.1_33312790 0/0
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## 3056 NW_019211460.1_33359694 <NA>
## 3057 NW_019211460.1_33366977 0/0
## 3058 NW_019211460.1_33379971 0/0
## 3059 NW_019211460.1_33404378 0/0
## 3060 NW_019211460.1_33415732 <NA>
## 3061 NW_019211460.1_33428353 <NA>
## 3062 NW_019211460.1_33429143 <NA>
## 3063 NW_019211460.1_33433042 <NA>
## 3064 NW_019211460.1_33495253 0/0
## 3065 NW_019211460.1_33522404 0/0
## 3066 NW_019211460.1_33522600 0/0
## 3067 NW_019211460.1_33527995 <NA>
## 3068 NW_019211460.1_33529980 <NA>
## 3069 NW_019211460.1_33530202 0/0
## 3070 NW_019211460.1_33534021 <NA>
## 3071 NW_019211460.1_33535617 <NA>
## 3072 NW_019211460.1_33917439 <NA>
## 3073 NW_019211460.1_34014687 0/0
## 3074 NW_019211460.1_34041959 <NA>
## 3075 NW_019211460.1_34043457 0/0
## 3076 NW_019211460.1_34058147 <NA>
## 3077 NW_019211460.1_34058870 0/0
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## 3079 NW_019211460.1_34070680 0/0
## 3080 NW_019211460.1_34126479 <NA>
## 3081 NW_019211460.1_34137993 0/0
## 3082 NW_019211460.1_34138303 0/0
## 3083 NW_019211460.1_34142318 0/0
## 3084 NW_019211460.1_34154512 <NA>
## 3085 NW_019211460.1_34165787 0/0
## 3086 NW_019211460.1_34205199 0/1
## 3087 NW_019211460.1_34217942 0/0
## 3088 NW_019211460.1_34218229 0/0
## 3089 NW_019211460.1_34232725 <NA>
## 3090 NW_019211460.1_34245101 <NA>
## 3091 NW_019211460.1_34261994 0/0
## 3092 NW_019211460.1_34262025 0/0
## 3093 NW_019211460.1_34268652 <NA>
## 3094 NW_019211460.1_34291766 <NA>
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## 3096 NW_019211460.1_34294018 0/0
## 3097 NW_019211460.1_34300158 0/0
## 3098 NW_019211460.1_34300503 0/0
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## 3100 NW_019211460.1_34308067 0/0
## 3101 NW_019211460.1_34309708 0/0
## 3102 NW_019211460.1_34318077 0/0
## 3103 NW_019211460.1_34324796 <NA>
## 3104 NW_019211460.1_34384750 0/0
## 3105 NW_019211460.1_34586356 <NA>
## 3106 NW_019211460.1_36084650 0/0
## 3107 NW_019211460.1_36089631 0/0
## 3108 NW_019211460.1_36106639 <NA>
## 3109 NW_019211460.1_36107351 <NA>
## 3110 NW_019211460.1_36116166 <NA>
## 3111 NW_019211460.1_36143041 0/0
## 3112 NW_019211460.1_36151630 0/0
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## 3114 NW_019211460.1_36170384 0/0
## 3115 NW_019211460.1_36183796 0/0
## 3116 NW_019211460.1_36185700 0/0
## 3117 NW_019211460.1_36186688 0/0
## 3118 NW_019211460.1_36189677 0/0
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## 3120 NW_019211460.1_36427509 0/0
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## 3122 NW_019211460.1_36495785 0/0
## 3123 NW_019211460.1_36495846 0/0
## 3124 NW_019211460.1_36496401 0/0
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## 3126 NW_019211460.1_36506890 0/0
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## 3128 NW_019211460.1_36506986 0/0
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## 3131 NW_019211460.1_36540823 0/0
## 3132 NW_019211460.1_36574100 0/0
## 3133 NW_019211460.1_36575932 <NA>
## 3134 NW_019211460.1_36583026 <NA>
## 3135 NW_019211460.1_36611771 0/0
## 3136 NW_019211460.1_36620185 0/0
## 3137 NW_019211460.1_36659362 <NA>
## 3138 NW_019211460.1_36682242 0/0
## 3139 NW_019211460.1_36701049 0/0
## 3140 NW_019211460.1_36719644 <NA>
## 3141 NW_019211460.1_36734940 <NA>
## 3142 NW_019211460.1_36735078 0/0
## 3143 NW_019211460.1_36741319 <NA>
## 3144 NW_019211460.1_36742951 0/0
## 3145 NW_019211460.1_36772888 0/0
## 3146 NW_019211460.1_36805687 <NA>
## 3147 NW_019211460.1_36826968 0/0
## 3148 NW_019211460.1_36827037 0/0
## 3149 NW_019211460.1_36831567 <NA>
## 3150 NW_019211460.1_36840509 0/0
## 3151 NW_019211460.1_36840579 <NA>
## 3152 NW_019211460.1_36845653 0/0
## 3153 NW_019211460.1_36850173 <NA>
## 3154 NW_019211460.1_36857838 0/0
## 3155 NW_019211460.1_36865521 <NA>
## 3156 NW_019211460.1_36871446 <NA>
## 3157 NW_019211460.1_36890857 <NA>
## 3158 NW_019211460.1_36932253 0/0
## 3159 NW_019211460.1_36960392 <NA>
## 3160 NW_019211460.1_36961695 <NA>
## 3161 NW_019211460.1_36963691 <NA>
## 3162 NW_019211460.1_36983052 0/0
## 3163 NW_019211460.1_37022696 0/0
## 3164 NW_019211460.1_37025392 0/0
## 3165 NW_019211460.1_37027355 <NA>
## 3166 NW_019211460.1_37030423 <NA>
## 3167 NW_019211460.1_37033340 0/0
## 3168 NW_019211460.1_37033695 0/0
## 3169 NW_019211460.1_38389758 0/0
## 3170 NW_019211460.1_38423641 0/0
## 3171 NW_019211460.1_38425397 <NA>
## 3172 NW_019211460.1_38501125 <NA>
## 3173 NW_019211460.1_38502540 <NA>
## 3174 NW_019211460.1_38534624 0/0
## 3175 NW_019211460.1_38581841 <NA>
## 3176 NW_019211460.1_38670263 0/0
## 3177 NW_019211460.1_38670283 0/0
## 3178 NW_019211460.1_38670376 0/0
## 3179 NW_019211460.1_38706950 <NA>
## 3180 NW_019211460.1_38721194 <NA>
## 3181 NW_019211460.1_38721858 0/0
## 3182 NW_019211460.1_38730978 <NA>
## 3183 NW_019211460.1_38757613 0/0
## 3184 NW_019211460.1_38777645 <NA>
## 3185 NW_019211460.1_38787839 <NA>
## 3186 NW_019211460.1_38858664 0/0
## 3187 NW_019211460.1_38860985 <NA>
## 3188 NW_019211460.1_38862382 0/1
## 3189 NW_019211460.1_39010532 <NA>
## 3190 NW_019211460.1_39017021 <NA>
## 3191 NW_019211460.1_39017101 <NA>
## 3192 NW_019211460.1_39026115 <NA>
## 3193 NW_019211460.1_39044960 0/0
## 3194 NW_019211460.1_39045225 <NA>
## 3195 NW_019211460.1_39076063 0/0
## 3196 NW_019211460.1_39150362 <NA>
## 3197 NW_019211460.1_39155077 0/0
## 3198 NW_019211460.1_39185495 0/0
## 3199 NW_019211460.1_39299544 <NA>
## 3200 NW_019211460.1_39301652 0/0
## 3201 NW_019211460.1_39380485 <NA>
## 3202 NW_019211460.1_39395023 <NA># join all grandkids of 26 families together
grnson0.0_0.0 <- reduce(list_grnson, full_join, by = "site") %>% view()
grndat0.0_0.0 <- reduce(list_grndat, full_join, by = "site") %>% view()
# it looks like the 'false sites' in one family, are not necessarily the false sites in other families. should i take them out?